Product Analytics Engineer
Nous Research is an applied AI research group that develops and releases open-source AI models, datasets, and tools to democratize artificial intelligence.
Maintainer signals as of 9/2/2026
Funding history
About Nous Research
Nous Research is a leader in the development of human-centric language models and simulators, with a primary focus on model architecture, data synthesis, fine-tuning, and reasoning to align AI with real-world user experiences. As an applied research group, they aim to democratize AI development by releasing open-source resources like datasets (e.g., Hermes 3 Dataset), AI models (e.g., Hermes 3, DeepHermes), and frameworks. A key project is the Psyche Network, an open infrastructure for AI development, and they also engage in on-chain evaluations, indicating an intersection with blockchain technology. Additionally, Nous Research provides an OpenAI-compatible API for its models, operating on a pay-per-use basis with pricing determined by token consumption.
Skills
About the Role
You will own product analytics from event design through recommendations. You will maintain event taxonomy and instrumentation, build data pipelines and dashboards, analyze funnels and retention, evaluate experiments, monitor metrics, investigate anomalies, and turn quantitative and qualitative insights into product improvements.
Requirements
- 5+ years of experience in product analytics, analytics engineering, data engineering, or a similar technical product role
- SQL
- Python
- TypeScript, JavaScript, or similar production application-code experience
- Analytics instrumentation experience across client applications and backend systems
- Experience with PostHog, Amplitude, or Mixpanel, data warehouses, and BI tools
- Knowledge of activation, engagement, retention, churn, experimentation, and funnel analysis
- Analytical problem-solving
- Communication
- AI-assisted development and analysis
Responsibilities
- Own product analytics from event design and instrumentation through analysis and recommendations
- Define and maintain event taxonomy and shared metrics
- Implement analytics instrumentation across applications, services, and integrations
- Build data pipelines, transformations, dashboards, and monitoring systems
- Analyze funnels, cohorts, retention, behavioral paths, and churn
- Segment user behavior to identify actionable insights
- Design and evaluate experiments
- Translate data into prioritized product improvements
- Build metric alerting systems and investigate anomalies
- Establish analytics, documentation, data-quality, and privacy-conscious telemetry practices